PneumoDoc 🩺

An app to detect Pneumonia using Deep Learning 🧠📱

• In this project two models ANN and CNN were built from scratch using TensorFlow and Keras
• The problem statement is to detect whether patient is having pneumonia or not by training models with x-ray images
• Data Augmentation was performed and the main reason behind it is to provide maximum randomness to the training data where we use a function to shuffle images, make minor changes, and save images accordingly because it is a good practice to provide as much randomness as possible in order to achieve good accuracy on real-time data and testing data
• Separable Convolutional layers were used which proved to be more robust and powerful
• After Comparing the accuracies of both the deep learning models best model was selected for further deployment on android application
• The model was converted in .tflite format in order to deploy it using TensorFlowlite dependencies in flutter
• UI of app was done using Flutter and Dart while functionalities were implemented using TensorFlowlite libraries
• The app enables user to click an image of x-ray or select the image from app storage or drive to check the results
• UI is configured in such a way that the result is displayed on the screen
• If patient has pneumonia a short and useful prescription from experienced doctors is made available in the app itself
• The models were built on Google Colab by fetching data through Kaggle API

⭐ DataSet

⭐ Training and Validating Accuracies 📉

• Artificial Neural Network Model

• Convolutional Neural Network Model

⭐ Train and Test Metrics

• Artificial Neural Network Model

• Convolutional Neural Network Model

⭐ User Interface using Flutter

⭐ Libraries

⭐ Flutter Dependencies

• tflite: ^1.0.2
• image_picker: ^0.8.4+4
• flutter_svg: ^0.23.0+1

⭐ Technologies used